Field of the Invention
The present disclosure relates to large-aperture, 3-dimensional spectroscopic LIDAR systems and methods. More specifically, the present disclosure relates to a standoff detection system that can detect areas of airborne material in a 3-dimensional envelope in parts per million (ppm) or lower concentration in the atmosphere.
Related Art
The accurate, real-time measurement of airborne materials, gases, and related aerosol or particulate compounds over a broad area is a persistent challenge in the monitoring of atmospheric and other environments. For example, the measurement of explosive and/or toxic gases can be vitally important for the safety of workers working in environments where such gases are commonly found, and even for the general population near or downwind of such environments. Ideally, measurements could be made in a non-contact, remote, or “standoff” mode and overall be rapid, cost effective, safe, and independent of atmospheric baseline concentrations of the analytes of interest, frequent calibration, or excessive human intervention. In addition to the concentration of the airborne contaminate, another important parameter is the determination of the flux of the contaminant over time, e.g., change in mass volume in a specific volume or specific discharge times of the concentration.
Standoff detection of volatile organic compounds is accomplished in the prior art by a wide range of technologies, including TDLAS (Tunable Diode Laser Spectroscopy), DIAL (Differential Absorption Lidar), FTIR (Fourier Transform Infrared) spectroscopy, and CM-CLADS (Chirped Mode Laser Dispersion Spectroscopy). However, none of these technologies are capable of providing a three dimensional (3D) profile of the atmospheric concentration of airborne compounds in the volumetric space above an area of interest. For example, none of the methods discussed above support a long-distance 3D mapping system for gas emissions and the requisite plume analysis and the resulting flux calculations, or even persistent standoff monitoring in a cost-effective manner. Further, none of these systems support long-term, self-calibrating, zero baseline, and path-independent features which are mandatory for unattended operation.
FIG. 1A is a topological view of a ground-based sensor network (e.g., a “ground truth” baseline method) of the prior art for measuring concentrations of volatile organic compounds (VOCs), in this case, benzene with a network of ground-based wireless sensors 2 placed in a 300 acre tank farm. The sensors 2 acquire data at 1 to 10 minute intervals and transmit analytical information along with meteorological information to a remote network site. FIG. 1B is a typical time-sequenced history of the benzene concentration at a particular sensor node 2 of the system shown in FIG. 1A. FIG. 1C illustrates a “time slice” of the measurement of FIG. 1B with superimposed meteorological information (e.g., wind velocities). The actual concentration of benzene and the corresponding time-resolved flux is computed and predicted from the point measurements, dispersion coefficients, and the factoring in of the effects of selected meteorological and topological features.
FIG. 2 is spectral diagram of numerous areas where a VOC such as benzene, for example, can be detected by the difference in absorption between benzene's normal isotope CH4 and atmospheric gases. A cluster of spectral bands around 3.41 and 5-6.7μ are shown.
FIGS. 3A and 3B illustrate a Differential Absorption Lidar (DIAL) detection and imaging system for mapping benzene clouds and plumes in the atmosphere. The system shown in FIG. 3A is used for persistent mapping of an area around a site of interest. FIG. 3B is a diagram depicting the various concentrations of benzene plumes in the atmosphere over a leaking gas production facility. The intensity of the clouds determines the relative concentration of the benzene, but is strongly dependent on atmospheric conditions including water vapor lines and the overall emissivity of the background.
FIGS. 4A-C are diagrams illustrating the state of the art capabilities for long-distance detection of an important greenhouse gas, methane, using mid-wavelength infrared (MWIR) hyperspectral imaging. MWIR hyperspectral imaging can detect methane clouds via absorption lines post event, but plume tracking is difficult. FIG. 4A is a diagram illustrating a 3.3μ absorption band 72 of the antisymetrical valence oscillation “vibrational” and a 7.7μ absorption band 74 of the deformation oscillation. FIG. 4B is a diagram illustrating that the 3.3μ absorption line 76 is close to the peak spectral response of the broadband MWIR camera. FIG. 4C illustrates a wide field of view image over a leaking gas platform.
The development of data using an ensemble of conventional techniques, as depicted in FIGS. 1A-3B, is limited by the distributed and episodic nature of fugitive benzene and other VOC emissions. Standard methodologies described above, as well as the accompanying atmospheric dispersion modeling and related mathematical simulations, are derived from point sampling methods that are predicated on well-mixed aerosols and gases. However, such models are severely compromised by disrupted wind flow fields, varied surface temperatures, and topologies of related manmade and natural structures.
Accordingly, what is needed are systems and methods for persistent surveillance of gases, volatile organic compounds, and airborne dispersed compounds that overcome the aforementioned deficiencies of the prior art.